基于“3S”技术的县级土地资源动态监测技术系统

土地资源日新月异的变化使得传统的土地资源管理手段无法满足土地资源信息及时更新的要求。探讨了利用遥感、地理信息系统和全球定位系统为代表的“3S”技术进行县级土地资源动态监测和更新的原理和方法。遥感是进行土地利用变化动态监测,发现土地利用变化区域的主要手段。全球定位系统接收仪用于对变化区域进行现场精确定位和实测。实测的变更数据可用于对原有的地理信息系统本底数据库进行修改,从而完成对土地资源的动态监测和数据库的及时更新。福清市土地资源动态监测技术系统就是利用“3S”技术建立起来的业务运行系统。系统所具备的功能可以满足县级土地资源的动态监测和及时更新。     

利用MODIS数据探测燃烧的方法研究

“3S”(遥感、地理信息系统和全球定位系统)技术在森林防火中的应用日益受到世界各国的重视。MODIS是美国NASA戈达德宇航中心在1999年12月18日发射的Terra(EOS-AM1)星上搭载的对地观测器之一。通过对Terra-MODIS数据的相关波段数据的分析,研究了不同波段在探测燃烧信息中的应用可能,并对MODIS数据在探测火点中的应用方法进行了研究;采用图像增强处理和多空间分辨率数据合成的方法,有效地提取了MODIS数据中的燃烧信息,同时也实现了MODIS数据中多空间分辨率数据间信息的融合。     

MODIS数据在树种长势监测中的应用

近年来,世界各国日益重视利用“3S”(遥感、地理信息系统和全球定位系统)技术对陆地表面植被进行研究。利用Terra-MODIS数据,分别采用了归一化植被指数(NDVI)、环境植被指数(EVI)、土壤调节植被指数(SAVI)以及比值植被指数(RVI)对实验区典型树种的长势进行了比较研究;同时对实验区典型树种的植被指数的地域变化和时间变化进行了分析,为探讨我国可燃物的时空变化规律打下了基础。     

退牧还草工程项目遥感分析与效益评价——以四川省阿坝县为例

根据对卫星遥感影像的判读解译,探讨了利用3S技术（遥感（RS）、全球定位系统（GPS）、地理信息系统（GIS）技术）监测四川省阿坝县的退牧还草工程现状。通过陆地卫星TM遥感影像数据和同期野外调查数据,分析了植被指数与草地植被生物量之间的相关关系,建立了不同植被指数与草地生物量之间的一元线性回归模型和非线性回归模型。结果表明,利用遥感卫星的植被指数可以较好反映牧草植被群落变化和不同草原类型的牧草产草量差异。在全年放牧草地中,地上总生物量、植被总覆盖度、植被平均高度等指标均低于围栏内的草地。因此,利用“3S”技术可以对全县草原地上生物量进行遥感估测并对草原基况做出评价,客观反映退牧还草工程实施后效果。同时,为推动高空间分辨率卫星影像在我国草业和生态环境建设中的应用打下了坚实基础。     

精准农业管理决策支持系统的设计与实现

“精准农业”是基于“3S”技术和农学知识支持的现代农业。而精准农业管理决策支持系统是实现“精准农业”的核心系统。为了解决精准农业中信息的获取、管理、分析和专家智能决策生成问题,研究并集成了精准农业中的关键技术--全球定位系统、地理信息系统、专家系统和决策支持系统。通过本系统,用户可以管理农田信息,进行有关品种、施肥、病虫害防治等决策支持,以实现农田的变量管理,减少生产成本和环境污染,增加经济效益。本系统采用C/S结构,具有可靠性、易扩充性和易操作性等特点。系统已在宁夏精准农业示范基地中得到部分应用。     

图像语义分割在智慧农业中的应用

计算机视觉与物联网技术的飞速发展,为农业生产带来了新机遇。智慧农业中的感知层设备可获取农业生产过程中的各类图像信息。将图像语义分割应用在农田场景识别中,可实现农业设备的自动导航和避障;应用在遥感图像分割中,可监控和管理土地资源;应用在作物分割识别中,不仅可以监测农作物的生长发育状态,还可以实现农作物的智能采摘等功能,为农业生产提供智能决策与控制。     

POLDER 多角度、多通道偏振探测器对地遥感观测研究进展

由法国空间研究中心(CNES) 于2004 年12 月18 日发射的PARASOL (Polarization &Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar) 卫星, 是法国和美国合作的“卫星列车”(“A-Train ”) 计划中的一员, 上面主要搭载了POLDER(Polarization and Directionality of the Earth’s Reflectances) 仪器, 可以通过全球观测, 从空间收集地气系统反射太阳辐射的偏振性和方向性数据。从这些数据, 我们可以获得地表、大气气溶胶及云的多种特性, 这些信息是改进气候数值模式所必需的。主要以POLDER 仪器为例, 就多光谱、多角度和多偏振联合遥感观测地气系统方面的进展进行综述。     

基于3S技术的荒漠化监测技术系统研究

武威市地处典型温带荒漠气候区,绿洲边缘地带的土地沙化、河流下游土地盐渍化和中低山草场的植被退化是荒漠化的主要表现形式。武威市荒漠化监测技术系统的构建,应以“3S”技术的综合与集成应用为主要技术手段,构建以矢量数据为主要数据结构形式的荒漠化空间数据管理系统,用于满足中大尺度荒漠化动态监测的需要为原则。该技术系统可分为数据的获取和输入、遥感影像处理和分类成图、空间数据管理以及监测结果输出等4个子系统。研究提出了荒漠化监测技术系统建立中适合当地荒漠化特点的荒漠化潜在发生范围确定、荒漠化土地类型划分体系、遥感影像荒漠化信息提取、基于DEM的坡耕地荒漠化评价等关键技术的解决方法。     

来自遥感、地理信息产业的声音

新一轮遥感应用的热潮徐智勇(航天星图科技(北京)有限公司总经理)随着人类生存环境的变化和国家发展竞争的日益激烈,对自然资源、地理资源和太空资源的开发和争夺已经成为影响人类和民族发展进程的重要因素。遥感正是为了满足这样的需求所产生的一门综合性应用技术,它为人类进行大规模资源探测及开发提供了技术手段,也为国家安全和发展提供了信息保障。目前遥感技术的发展趋势是向遥感信息定量化、信息处理智能化、数据获取动态化、遥感应用网络化、遥感工具实用化的方向发展,由此带来了新一轮遥感应用的热潮。作为技术型的企业,航天星图建…     

北京奥运主场馆区航空真彩色影像的变化检测与分析

中国科学院遥感应用研究所在科技部“科技奥运”专项经费和中科院创新经费的支持下设立了“奥运环境动态监测”课题, 计划从2001 年开始到2008 年每年6~7月航飞一次, 获取真彩色影像、统计分析年施工进度, 将变化信息提供给奥组委和网上发布。介绍了奥运主场馆区2001 年、2002 年和2003 年3 年变化信息的检测技术方法和统计结果。     

Monitoring desertification and land degradation over sub-Saharan Africa

A desertification monitoring system is developed that uses four indicators derived using continental-scale remotely sensed data: vegetation cover, rain use efficiency (RUE), surface run-off and soil erosion. These indicators were calculated on a dekadal time step for 1996. Vegetation cover was estimated using the Normalized Difference Vegetation Index (NDVI). The estimation of RUE also employed NDVI and, in addition, rainfall derived from Meteosat cold cloud duration data. Surface run-off was modelled using the Soil Conservation Service (SCS) model parametrized using the rainfall estimates, vegetation cover, land cover, and digital soil maps. Soil erosion, one of the most indicative parameters of the desertification process, was estimated using a model parametrized by overland flow, vegetation cover, the digital soil maps and a digital elevation model (DEM). The four indicators were then combined to highlight the areas with the greatest degradation susceptibility. The system has potential for near-real time monitoring and application of the methodology to the remote sensing data archives would allow both spatial and temporal trends in degradation to be determined.     

Verification of the analysis method for extracting the spatial continuity of the vegetation distribution on a regional scale

Vegetation plays a key role in not only improving urban environments, but also conserving ecosystems. The spatial continuity of vegetation distributions can be expected to make green corridors for landscape management, wind paths against heat island phenomena. In this paper, we develop a spatial analysis method of vegetation distributions using remotely sensed data on a regional scale. The method consists of a spatial autocorrelation analysis, an overlay analysis, and a hydrological analysis with the Normalized Difference Vegetation Index (NDVI) adopted as the proxy of vegetation abundance. Application of the method leads to the extraction of the lines between the core areas and sparse areas of vegetation. The purpose of this study is to verify our method through applying a vegetation map digitized from aerial photographs. The map contained three vegetation types of land cover: grasslands, agricultural fields, and tree-covered areas. We use remotely sensed data collected at four different time periods at the regional scale, along with information on the seasonal fluctuations of the vegetation. As a result, the exclusion of seasonal land-cover changes, as in the reaping of agricultural fields, in the process of applying the proposed method produces an effect. The analysis reveals steady areas unaffected by the seasonal fluctuation of vegetation along the lines extracted by applying the proposed method.     

Comparison between vegetation change analysis in Kenya based on AVHRR and SeaWiFS images

An assessment of the suitability of the Advanced Very High Resolution Radiometer (AVHRR) vegetation index to estimate land degradation in semi‐arid areas has been carried out, comparing its behaviour with that of vegetation indices based on Sea‐viewing Wide Field‐of‐view Sensor (SeaWiFS) images. Notwithstanding the importance of the classic Normalized Difference Vegetation Index (NDVI) indicator, based on red–NIR channels, several studies have identified some limitations related to its use, such as its dependence on the atmospheric profile, saturation problems, non‐linearity in biophysical coupling with Leaf Area Index (LAI) and canopy background contamination. The relatively recent Enhanced Vegetation Index (EVI) overcomes these limits, using the information related to the blue channel and a soil adjustment factor. SeaWiFS data allow the computation of both vegetation indices. On the other hand, the NDVI based on AVHRR can be computed back in time to the 1980s, allowing a sufficient time span to obtain information on the desertification trend of the considered region (northern Kenya). In conclusion, taking advantage of both datasets, the accuracy of a change detection technique based on the classic NDVI has been confirmed as suitable for revealing any desertification trend.     

Global land cover classification at 1 km spatial resolution using a classification tree approach

This paper on reports the production of a 1 km spatial resolution land cover classification using data for 1992-1993 from the Advanced Very High Resolution Radiometer (AVHRR). This map will be included as an at-launch product of the Moderate Resolution Imaging Spectroradiometer (MODIS) to serve as an input for several algorithms requiring knowledge of land cover type. The methodology was derived from a similar effort to create a product at 8 km spatial resolution, where high resolution data sets were interpreted in order to derive a coarse-resolution training data set. A set of 37 294 x 1 km pixels was used within a hierarchical tree structure to classify the AVHRR data into 12 classes. The approach taken involved a hierarchy of pair-wise class trees where a logic based on vegetation form was applied until all classes were depicted. Multitemporal AVHRR metrics were used to predict class memberships. Minimum annual red reflectance, peak annual Normalized Difference Vegetation Index (NDVI), and minimum channel three brightness temperature were among the most used metrics. Depictions of forests and woodlands, and areas of mechanized agriculture are in general agreement with other sources of information, while classes such as low biomass agriculture and high-latitude broadleaf forest are not. Comparisons of the final product with regional digital land cover maps derived from high-resolution remotely sensed data reveal general agreement, except for apparently poor depictions of temperate pastures within areas of agriculture. Distinguishing between forest and non-forest was achieved with agreements ranging from 81 to 92% for these regional subsets. The agreements for all classes varied from an average of 65% when viewing all pixels to an average of 82% when viewing only those 1 km pixels consisting of greater than 90% one class within the high-resolution data sets.     

A comparative study of NOAA-AVHRR derived drought indices using change vector analysis

The objective of this study was to compare the spatial occurrences of droughts, detected by remotely sensed drought-indices over the desert-steppe and desert geo-botanical zones of Mongolia. All indices were derived from reflectance and thermal data sets, obtained from the NOAA-AVHRR data between 1982 and 1999. One group of the drought-indices is based on vegetation state derived from the reflective channels. This group includes the Normalized Difference Vegetation Index (NDVI), Anomaly of Normalized Difference Vegetation Index (NDVIA), Standardized Vegetation Index (SVI), and Vegetation Condition Index (VCI). Another group, based on surface brightness temperature derived from the thermal channel of NOAA-AVHRR, includes the Temperature Condition Index (TCI). The third group is based on combination between the reflective and thermal channels includes the ratio between Land Surface Temperature (LST) and NDVI (LST/NDVI), the Vegetation Health Index (VH), and the Drought Severity Index (DSI). Change detection procedure was performed by using the Change Vector Analysis in the temporal domain. Comparison analysis among the drought-indices reveals that there is no spatial coincidence between them, even when the vegetation growing period was divided into 2-month sub-periods — beginning, middle, and end. Based on the statistical analysis, higher correlations were found among the reflective indices while lesser or no relationships were found between the thermal and combination of the thermal and reflective indices. Furthermore, no agreement was found between the spatial extent of the satellite-derived drought-indices and the meteorological-based Palmer Drought Severity Index (PDSI) and also between the traditional ground-observed drought-affected-areas (DAA) maps. It was found that the combination of satellite-derived drought-indices can identify wider drought-occurred areas rather than the PDSI and the DAA maps. In summary, this study concludes that it is difficult to point out the most reliable drought index, and that the ground observations cannot provide sufficient information for validation of satellite derived drought indices.     

Optimum pixel size for hyperspectral studies of ecosystem function in southern California chaparral and grassland

Hyperspectral remotely sensed data are useful for studying ecosystem processes and patterns. However, spatial characterization of such remotely sensed images is needed to optimize sampling procedures and address scaling issues. We have investigated spatial scaling in ground-based and airborne hyperspectral data for canopy- to watershed-level ecosystem studies of southern California chaparral and grassland vegetation. Three optical reflectance indices, namely, Normalized Difference Vegetation Index (NDVI), Water Band Index (WBI) and Photochemical Reflectance Index (PRI) were used as indicators of biomass, plant water content and photosynthetic activity, respectively. Two geostatistical procedures, the semivariogram and local variance, were used for the spatial scaling analysis of these indices. The results indicate that a pixel size of 6 m or less would be optimal for studying functional properties of southern California grassland and chaparral ecosystems using hyperspectral remote sensing. These results provide a guide for selecting the spatial resolution of future airborne and satellite-based hyperspectral sensors.     

Correction of the effect of Sun-sensor-target geometry in NOAA AVHRR data

Models of determining the effects of the bidirectional reflectance distribution function (BRDF) of different surfaces and of eliminating the effect of Sun-sensor-target geometry from the remotely sensed satellite data are actual. The objective of this study is to develop a simple relation between the Sun-sensor-target geometry and the remotely sensed vegetation index. In this investigation 238 National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) images were used over Hungary during the period 1996-98. The greenness vegetation index (the difference between the reflectance values of near-infrared and visible channels) was used between days of the year 140-200, because the greenness values can be considered as constant in this period over the agricultural areas. The so-called 'hot spot effect' can be observed in the variation of reflectance values with different viewing zenith angles of the sensor. A simple quadratic relation was found between the raw AVHRR greenness values and the angle enclosed by the Sun-target and target-sensor directions over the agricultural areas, forests and grasslands. A correction method was developed to eliminate the effect of the Sun-sensor-target geometry, which it is hoped will improve the accuracy of yield forecasting and estimation procedures using NOAA AVHRR data.     

Land-cover classification of China: Integrated analysis of AVHRR imagery and geophysical data

Over last two decades, numerous studies have used remotely sensed data from the Advanced Very High Resolution Radiometer (AVHRR) sensors to map land use and land cover at large spatial scales, but achieved only limited success. In this paper, we employed an approach that combines both AVHRR images and geophysical datasets (e.g. climate, elevation). Three geophysical datasets are used in this study: annual mean temperature, annual precipitation, and elevation. We first divide China into nine bio-climatic regions, using the long-term mean climate data. For each of nine regions, the three geophysical data layers are stacked together with AVHRR data and AVHRR-derived vegetation index (Normalized Difference Vegetation Index) data, and the resultant multi-source datasets were then analysed to generate land-cover maps for individual regions, using supervised classification algorithms. The nine land-cover maps for individual regions were assembled together for China. The existing land-cover dataset derived from Landsat Thematic Mapper (TM) images was used to assess the accuracy of the classification that is based on AVHRR and geophysical data. Accuracy of individual regions varies from 73% to 89%, with an overall accuracy of 81% for China. The results showed that the methodology used in this study is, in general, feasible for large-scale land-cover mapping in China.     

Sharpened Mapping of Tropical Forest Biophysical Properties from Coarse Spatial Resolution Satellite Sensor Data

Forest biophysical properties are typically estimated and mapped from remotely sensed data through the application of a vegetation index. This generally does not make full use of the information content of the remotely sensed data, using only the data acquired in a limited number of spectral channels, and may provide a relatively crude spatial representation of the biophysical variable of interest. Using imagery acquired by the NOAA AVHRR, it is shown that a standard neural network may use all the spectral channels available in a remotely sensed data set to derive more accurate estimates of the biophysical properties of tropical forests in Ghana than a series of vegetation indices. Additionally, the spatial representation derived can be refined by fusion with finer spatial resolution imagery, achieved with the application of a further neural network.